How to Use a Semantic Layer on Big Data to Drive AI & BI Impact

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Learn about using a semantic layer to make data accessible and how to accelerate the business impact of AI and BI at your organization. This session will offer practical advice on how to drive AI & BI business outcomes with an effective data strategy that leverages a semantic layer. You will learn how to achieve quantifiable results by modernizing your data and analytics stack with a semantic layer that delivers an order of magnitude better query performance, increased data team productivity, lower query compute costs, and improved Speed-to-Insights. Attend this session to learn about: - Gaining business alignment and reducing data prep for your AI and BI teams. - Making a consistent set of business metrics “analytics-ready” and accessible. - Accelerating end-to-end query performance while optimizing cloud resources. - Treating “data as a product” and how to drive business value for all consumers.

Données & analyses

How to use a semantic
layer on Big Data to
drive AI & BI impact
David P. Mariani, CTO & Founder
Dataversity

A semantic layer provides
the agility, consistency,
and control needed to
scale enterprise BI and AI
3

What a Semantic Layer looks like in real life
4
The Semantic Model Tableau Excel Power BI

What does a Semantic Layer do?
CONSUMPTION INTEGRATION
DATA INTEGRATION
Connect consumption tools to data LIVE
Connect to data platforms, abstract location & format of data
SEMANTIC
MODELING
MULTI-DIMENSIONAL
CALCULATION ENGINE
PERFORMANCE
OPTIMIZATION
ANALYTICS
GOVERNANCE
Define business-friendly
data models
Support complex, cell-based
expressions
Optimize performance and
cost
Enforce access control and
data policies
5

How is a
semantic layer
used?
CLOUD ANALYTICS OPTIMIZATION
Speed of thought analytics on live
cloud data, agile data integration with
minimal data movement.
ENTERPRISE METRIS STORE
Single source of governed enterprise
metrics that can be self-served from
any AI/BI tool.
BRIDGING AI AND BI
Common view of data assets and
platform for publishing AI-generated
insights to decision makers.
OLAP MODERNIZATION
Migrate legacy OLAP (e.g. SSAS)
models to modern cloud-first
infrastructure.
6

Where does a semantic layer fit in the stack?
7
DATA CONSUMPTION
SEMANTIC LAYER
DATA API LAYER
DATA PREPARATION
RAW DATA
BI TOOLS AI/ML TOOLS APPLICATIONS
CLOUD DATA
WAREHOUSE
DATA LAKE
ENGINES
SAAS APIs
DATA
TRANSFORMATION
TOOLS
DATA LAKES
SAAS
APPLICATIONS
DATA
CATALO
G

The Value a
Semantic Layer
Provides
Control the Complexity
and Cost of Analytics
Simplify & Accelerate
Self-service
Provide Consistency &
Governance for AI & BI
8

TPC-DS 10TB Benchmark: Improvements with AtScale
1. Elapsed time for executing 1 query five times
2. Elapsed time executing 1 (x5), 5, 25, 50 queries
3. Compute costs for cluster time (Redshift, Snowflake) or bytes read (BigQuery) for user concurrency test
4. Complexity score for SQL queries for number of: functions, operations, tables, objects & subqueries (AtScale = 258, TPC-DS = 1,057)
Test
Improvement Factor with AtScale
BigQuery Redshift Snowflake Synapse
Query
Performance1 4x Faster 11x Faster 3x Faster 6x Faster
User Concurrency2 11x Faster 31x Faster 12x Faster 11x Faster
Improved ROI3 3x Better 4x Better 16x Better 2x Better
Complexity4 76% less complex SQL queries
9

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Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends. Specific learning objectives include: - Understanding what types of challenges require data modeling to be part of the solution - How automation requires standardization on derivable via data modeling techniques - Why only a working partnership between data and the business can produce useful outcomes

Data Modeling Fundamentals

DATAVERSITY

Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like. Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements. This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.

Showing ROI for Your Analytic Project

DATAVERSITY

Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there. Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups: - why it matters, - what it means, - what gets in the way, - who needs it (and how much they need), - what companies believe it will accomplish. In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.

Is Enterprise Data Literacy Possible?

DATAVERSITY

Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations. Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value. Join us for this panel discussion with experts in these fields as they discuss: - Recent research about where data privacy, security and governance stand - The most valuable enterprise data use cases - The common obstacles to data value creation - New approaches to data privacy, security and governance - Their advice on how to shift from a reactive to resilient mindset/culture/organization You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

DATAVERSITY

Emerging Trends in Data Architecture – What’s the Next Big Thing?

DATAVERSITY

As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance. Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance. In this webinar, Bob will focus on: - Data Governance’s past, present, and future - How trials and tribulations evolve to success - Leveraging lessons learned to improve productivity - The great Data Governance tool explosion - The future of Data Governance

Data Governance Trends - A Look Backwards and Forwards

DATAVERSITY

Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind. In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving. Topics that you will hear addressed in this webinar: - Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends - Best practices for key management and apply data governance to your day-to-day - The next wave of Confidential Computing and how to get started, including a demo

Data Governance Trends and Best Practices To Implement Today

DATAVERSITY

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

2023 Trends in Enterprise Analytics

DATAVERSITY

Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Data Strategy Best Practices

DATAVERSITY

The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

Who Should Own Data Governance – IT or Business?

DATAVERSITY

It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

Data Management Best Practices

DATAVERSITY

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

MLOps – Applying DevOps to Competitive Advantage

DATAVERSITY

With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered. Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence. Topics you will hear addressed in this webinar: Data observability – what is it and how it can complement your data quality strategy Why now is the time to incorporate data observability into your DataOps strategy How data observability helps prevent data issues from impacting downstream analytics Examples of how data observability can be used to prevent real-world issues

Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...

DATAVERSITY

Plus de DATAVERSITY (20)

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...

Data at the Speed of Business with Data Mastering and Governance

Exploring Levels of Data Literacy

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Make Data Work for You

Data Catalogs Are the Answer – What is the Question?

Data Catalogs Are the Answer – What Is the Question?

Data Modeling Fundamentals

Showing ROI for Your Analytic Project

Is Enterprise Data Literacy Possible?

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

Emerging Trends in Data Architecture – What’s the Next Big Thing?

Data Governance Trends - A Look Backwards and Forwards

Data Governance Trends and Best Practices To Implement Today

2023 Trends in Enterprise Analytics

Data Strategy Best Practices

Who Should Own Data Governance – IT or Business?

Data Management Best Practices

MLOps – Applying DevOps to Competitive Advantage

Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...

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How fast can you modify your data collection to include a new field, make all the necessary changes in data processing and storage, and then use that field in analytics or product features? For many companies, the answer is a few quarters, whereas others do it in a day. This data agility latency has a direct impact on companies' ability to innovate with data. Schema-on-read has been a key strategy to lower that latency - as the community has shifted towards storing data outside relational databases, we no longer need to make series of schema changes through the whole data chain, coordinated between teams to minimise operational risk. Schema-on-read comes with a cost, however. Errors that we used to catch during testing or in early test deployments can now sneak into production undetected and surface as product errors or hard-to-debug data quality problems later than with schema-on-write solutions. In this presentation, we will show how we have rejected the tradeoff between slow schema change rate and quality to achieve the best of both worlds. By using metaprogramming and versioned pipelines that are tested end-to-end, we can achieve fast schema changes with schema-on-write and the protection of static typing. We will describe the tools in our toolbox - Scalameta, Chimney, Bazel, and custom tools. We will also show how we leverage them to take static typing one step further and differentiate between domain types that share representation, e.g. EmailAddress vs ValidatedEmailAddress or kW vs kWh, while maintaining harmony with data technology ecosystems.

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How to Use a Semantic Layer on Big Data to Drive AI & BI Impact

1. How to use a semantic layer on Big Data to drive AI & BI impact David P. Mariani, CTO & Founder Dataversity

2. 2

3. A semantic layer provides the agility, consistency, and control needed to scale enterprise BI and AI 3

4. What a Semantic Layer looks like in real life 4 The Semantic Model Tableau Excel Power BI

5. What does a Semantic Layer do? CONSUMPTION INTEGRATION DATA INTEGRATION Connect consumption tools to data LIVE Connect to data platforms, abstract location & format of data SEMANTIC MODELING MULTI-DIMENSIONAL CALCULATION ENGINE PERFORMANCE OPTIMIZATION ANALYTICS GOVERNANCE Define business-friendly data models Support complex, cell-based expressions Optimize performance and cost Enforce access control and data policies 5

6. How is a semantic layer used? CLOUD ANALYTICS OPTIMIZATION Speed of thought analytics on live cloud data, agile data integration with minimal data movement. ENTERPRISE METRIS STORE Single source of governed enterprise metrics that can be self-served from any AI/BI tool. BRIDGING AI AND BI Common view of data assets and platform for publishing AI-generated insights to decision makers. OLAP MODERNIZATION Migrate legacy OLAP (e.g. SSAS) models to modern cloud-first infrastructure. 6

7. Where does a semantic layer fit in the stack? 7 DATA CONSUMPTION SEMANTIC LAYER DATA API LAYER DATA PREPARATION RAW DATA BI TOOLS AI/ML TOOLS APPLICATIONS CLOUD DATA WAREHOUSE DATA LAKE ENGINES SAAS APIs DATA TRANSFORMATION TOOLS DATA LAKES SAAS APPLICATIONS DATA CATALO G

8. The Value a Semantic Layer Provides Control the Complexity and Cost of Analytics Simplify & Accelerate Self-service Provide Consistency & Governance for AI & BI 8

9. TPC-DS 10TB Benchmark: Improvements with AtScale 1. Elapsed time for executing 1 query five times 2. Elapsed time executing 1 (x5), 5, 25, 50 queries 3. Compute costs for cluster time (Redshift, Snowflake) or bytes read (BigQuery) for user concurrency test 4. Complexity score for SQL queries for number of: functions, operations, tables, objects & subqueries (AtScale = 258, TPC-DS = 1,057) Test Improvement Factor with AtScale BigQuery Redshift Snowflake Synapse Query Performance1 4x Faster 11x Faster 3x Faster 6x Faster User Concurrency2 11x Faster 31x Faster 12x Faster 11x Faster Improved ROI3 3x Better 4x Better 16x Better 2x Better Complexity4 76% less complex SQL queries 9

10. www.atscale.co m

How to Use a Semantic Layer on Big Data to Drive AI & BI Impact

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How to Use a Semantic Layer on Big Data to Drive AI & BI Impact